Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval

Liu, Peizhong; Guo, Jing-Ming; Wu, Chi-Yi; Cai, Danlin

doi:10.1109/tip.2017.2736343

Cited by 128 publications

(66 citation statements)

References 45 publications

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“…More applications of deep learning can be seen in the work by [16] which used deep learning for fast cover song retrieval and the work by [17] applied deep learning for multimedia retrieval. The research carried out by [18] as well as [19] applied the deep learning method, CNN to improve the performance of image retrieval. In addition, [ 20 ] integrated deep learning methods for reaction creation also known as response generation into a customer service chatbot.…”

Section: Related Workmentioning

confidence: 99%

An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

Leong*¹,

Goh²,

Kumar³

2019

IJITEE

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In accordance to the Fourth Industrial Revolution, the emergence of the digital transformation alongside with the collaboration between networked machines and human beings in decision-making has attracted much attention globally. The utilization of conversational agent to convey information on behalf of human have gained popularity and been used extensively. As a result, this research intends to examine and to innovate the current corporate website by providing the capabilities to deliver instantaneously replies and reliable information through the integration of a conversational agent via deep learning, comparable to communicating with the competent customer service consultant. This study is reliant on the Artificial Intelligence (AI) to offer natural language processing (NLP) by presenting retrieval-based model and Deep Learning to enable the conversation agent to make smarter and better decision in generating reliable and up-to-date responses. Furthermore, this research intends to automate the process of minimizing the need for human interference, specifically the botmaster to perform the knowledge maintenance manually.

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Section: Related Workmentioning

confidence: 99%

An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

Leong*¹,

Goh²,

Kumar³

2019

IJITEE

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“…Seperti titik pusat mata, tepi mulut, hidung dan berbagai titik dengan ukuran jarak antara satu sama lain. Titik fiducial ini sangat penting menyeleksi bagian dari wajah [12], [13]. Selain itu titik fiducial ini dapat digunakan untuk transformasi posisi wajah seperti melakukan rotasi, menggeser posisi, refleksi dan mengubah skala wajah.…”

Section: Pendahuluanunclassified

Pengujian Algoritma MTCNN (Multi-task Cascaded Convolutional Neural Network) untuk Sistem Pengenalan Wajah

Pratama

Istoningtyas

Rasywir

2019

mib

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Measurement of facial similarity or checking similarity is done using features. The algorithm for describing the most up-to-date and best face features for generating features is Deep Convolutional Neural Network (DCNNs). Based on this, this study uses MTCNN (Multi-task Cascaded Convolutional Neural Network) as one variation of the DCNN method. In this research, we built a research system to test results with javascript. Given the many needs that are based on mobile or can be run on a smartphone. One of them is to support the absent feature that is used in a mobile manner such as the reporting system of sales and marketing performance or members of the police personnel who normally work on a mobile basis. From the results of the tests carried out automatically using several variation models testing the image of the Aberdeen dataset as many as 60 images from 30 different people used in the face recognition research system using MTCNN with influencing image parameters such as lighting variations, object position variations, then the position taken and expression face on the object image, the research system managed to do face recognition by 100%. Thus, true positive values are equal to the amount of data tested and zero negative true values.

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“…Since noises are mostly located in the high-frequency subband, the bandpass sub-image is filtered by the median, which is beneficial to remove the high-frequency noise and perform the mathematical morphology calculation. Nonlinear mapping function [21,22] removes isolated dots and burrs and preserves the overall shape of the image. Formula (1) express the nonlinear mapping function.…”

Section: Adaptive Multi-threshold Segmentationmentioning

confidence: 99%

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

et al. 2019

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Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.

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Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval

Cited by 128 publications

References 45 publications

An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

An Embodied Conversational Agent using Retrieval-Based Model and Deep Learning

Pengujian Algoritma MTCNN (Multi-task Cascaded Convolutional Neural Network) untuk Sistem Pengenalan Wajah

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

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